Title: Construction of a GA-RBF-based early warning model for corporate financial risk in the context of sustainable development

Authors: Jingjing Gong; Hongwen Han; Zhen Lv

Addresses: College of Business, Yantai Nanshan University, Yantai, 265713, China ' College of Engineering, Yantai Nanshan University, Yantai, 265713, China ' Shandong Nanshan Aluminium Co., Ltd., Yantai, 265718, China

Abstract: Financial risk indicators will have a negative impact on the development planning of enterprises, so the research introduces the theory of genetic algorithm. The result is that the overall performance of the model based on GA-RBF is superior to that of the model based on BF, CNN and RBF. GA-RBF model reaches a stable state when the number of training is 120, and the speed is significantly faster than the other three models. The error value of GA-RBF model is significantly lower than other model, and the error reduction speed is also faster. The time and memory of the four models increase with the increase of the number of samples, but the time and memory of GA-RBF model is less than the other three models. The highest prediction accuracy of GA-RBF model is 91.25%, and the highest prediction accuracy of RBF neural network is 64.5%.

Keywords: sustainable development; GA-RBF; corporate finance; risk warning.

DOI: 10.1504/IJNVO.2023.133845

International Journal of Networking and Virtual Organisations, 2023 Vol.28 No.2/3/4, pp.184 - 198

Received: 15 Aug 2022
Accepted: 14 Jan 2023
Published online: 04 Oct 2023 *

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